Privacy-Preserving, Thermal Vision With Human in the Loop Fall Detection Alert System

Abdallah Naser (Corresponding Author), Ahmad Lotfi, Maria Drolence Mwanje, Junpei Zhong

Research output: Journal article publicationJournal articleAcademic researchpeer-review


To support the independent living of older adults in their own homes, it is essential to identify their abnormal behaviors before triggering an automated alert system. Existing normal vision sensing approaches to detect human falls in the activities of daily living (ADL) experienced acceptability issues due to outstanding privacy concerns when they are deployed in personal environments. Besides, false alerts (false-positive) fall detection has not been addressed thoroughly in systems that report abnormal human behaviors as emergency alerts to the information support. This article proposes a novel human-in-the-loop fall detection approach in the ADLs using a low-resolution thermal sensor array. The motivation for enabling a human interactive model, fall detection confirmation, is to influence resource efficiency by reducing false-positive alerts while keeping the false-negative fall predictions as low as possible. The proposed approach is based on the motion sequence classification of human movements using a recurrent neural network. The proposed approach is evaluated with comprehensive experiments using different learning techniques, users, and domestic environment conditions. This article shows a performance accuracy of 99.7% to detect human falls from various typical ADLs.

Original languageEnglish
Pages (from-to)1-12
Number of pages12
JournalIEEE Transactions on Human-Machine Systems
Publication statusPublished - 9 Sept 2022


  • Activities of daily living (ADL) recognition
  • anomaly detection
  • Fall detection
  • fall detection
  • Feature extraction
  • Histograms
  • Human in the loop
  • human-in-the-loop
  • machine learning
  • Older adults
  • optical flow
  • privacy-preserving approach
  • Sensors
  • Temperature sensors
  • thermal sensor array (TSA)

ASJC Scopus subject areas

  • Human Factors and Ergonomics
  • Control and Systems Engineering
  • Signal Processing
  • Human-Computer Interaction
  • Computer Science Applications
  • Computer Networks and Communications
  • Artificial Intelligence


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